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Projects / Programmes source: ARIS

Minlp techniques for hierarchical-multilevel topology and parameter optimization of chemical processes

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Research activity

Code Science Field Subfield
2.02.03  Engineering sciences and technologies  Chemical engineering  Process system engineering 

Code Science Field
T350  Technological sciences  Chemical technology and engineering 
T120  Technological sciences  Systems engineering, computer technology 
Keywords
MINLP optimization, process optimization, hierarchical strategy, multilevel MINLP optimization, synthesis under uncertainty, process flexibility, hybrid modelling environment
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (4)
no. Code Name and surname Research area Role Period No. of publications
1.  03009  PhD Branko Butinar  Chemical engineering  Researcher  1999 - 2000  141 
2.  06005  PhD Zdravko Kravanja  Chemical engineering  Head  1999 - 2000  903 
3.  11369  PhD Zorka Novak Pintarič  Chemical engineering  Researcher  1998 - 2000  476 
4.  03032  MSc Franc Purkeljc  Chemical engineering  Researcher  1999 - 2000  26 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  0794  University of Maribor, Faculty of Chemistry and Chemical Engineering  Maribor  5089638012  13,118 
Abstract
The main objective is to develop cost effective mixed-integer nonlinear programming (MINLP) techniques for simultaneous topology and parameter optimization of chemical processes. In order to address real industrial problems and to gained additional profit and flexible designs, the hierarchical strategy of process synthesis following task identification, task targeting and task integration will be simultaneously considered in the multilevel MINLP optimization approach. Techniques will be proposed to apply the strategy under uncertainty using more rigorous and more detailed models formulated in hybrid modelling environment.
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